Machine vision systems (also termed “vision systems”) that perform measurement, inspection, alignment of objects and/or decoding of symbology (e.g. bar codes—also termed “IDs”) are used in a wide range of applications and industries. Such IDs are applied in a variety of formats (e.g. one-dimensional (1D), two-dimensional (2D), QR-code, DataMatrix, DotCode, etc.). These systems are based around the use of an image sensor (or “imager”), which acquires images (typically grayscale or color, and in one, two or three dimensions) of the subject or object, and processes these acquired images using an on-board or interconnected vision system processor. The processor generally includes both processing hardware and software, in the form of non-transitory computer-readable program instructions, which perform one or more vision system processes to generate a desired output based upon the image's processed information. This image information is typically provided within an array of image pixels each having various colors and/or intensities. In the example of an ID reader (also termed herein, a “camera”), the user or automated process acquires an image of an object that is believed to contain one or more barcodes. The image is processed to identify barcode features, which are then decoded by a decoding process and/or processor obtain the inherent alphanumeric data represented by the code.
A common use for ID readers is to track and sort objects (e.g. packages) moving along a line (e.g. a conveyor) in manufacturing and logistics operations. The ID reader can be positioned over the line at an appropriate viewing angle and distance to acquire any expected IDs on respective objects as they each move through the field of view. The focal distance of the reader with respect to the object can vary, depending on the placement of the reader with respect to the line and the size of the object. That is, a taller object may cause IDs thereon to be located closer to the reader, while a lower/flatter object may contain IDs that are further from the reader. In each case, the ID should appear with sufficient resolution to be properly imaged and decoded. Thus, the field of view of a single reader, particularly in with widthwise direction (perpendicular to line motion) is often limited. Where an object and/or the line is relatively wide, the lens and sensor of a single ID reader may not have sufficient field of view in the widthwise direction to cover the entire width of the line while maintaining needed resolution for accurate imaging and decoding of IDs. Failure to image the full width can cause the reader to miss IDs that are outside of the field of view.
In certain cases, the field of view of the camera system can be widened (in a direction transverse to motion), often while narrowing the resolution (number of image pixels) in the motion direction by implementing a field of view (FOV) expander. One such expander system is shown and described, by way of useful background in U.S. Published Patent Application No. US-2013-0201563-A1, entitled SYSTEM AND METHOD FOR EXPANSION OF FIELD OF VIEW IN A VISION SYSTEM.
However this approach is cumbersome in many applications and is often more suited to situations where the camera system must image a relatively wide line, rather than a line that includes both higher and lower boxes.
The problem is further illustrated in FIG. 1, which shows a camera assembly 100 that is aligned along an optical axis OA with respect the a moving surface S. The surface moves in the direction of the page, and thus the width across the line is shown in this example. The camera assembly 100 acquires an image of a scene containing a box or similar object 110 with a top surface 112 that can contain one or more IDs requiring decoding by the camera system and associated vision system processes 120 (including an ID-decoding process 122). The height HB of the subject box 110 is shown as well as the width WB. In runtime, the actual height of the box can be varied between approximately 0 (a flat object on the conveyor S) and a maximum height (approximately HB). The viewing angle α of the camera assembly 100 should be set to image the full width of the box at a given maximum height. Thus, a viewing angle a1 sufficiently covers the entire top surface 112 of the box 110. However, by setting the viewing angle to a1, the field of view is significantly larger (often 1.5 to 2 times larger) than the field of view needed to cover the dimensions of a flatter object with a height closer to 0 and width WB. These “wasted” image pixels on the opposing ends of the image sensor's field of view is indicated by boundary points 130. Thus, to adequately image a flatter object, a narrower viewing angle a2 can be employed.
Typically, there is a similar “waste” of pixels for flatter objects along the transport direction (into and out of the page of FIG. 1) due to the need for a narrower viewing angle. In addition, waste of image pixels in the transport direction results because the required filed of view along this dimension is generally defined by the feature that must be captured in an image. That is, the image should span the entire width of the object, but need only be tall enough to capture the height of the particular features found within the overall object width (e.g. an ID). This is, because multiple images are captured as the object passes under the camera (in the transport direction), and at least one or more of the captured images will contain the feature. In a typical logistics ID-reading application, the length of a barcode is usually 75 mm, so a field of view of 100 mm would sufficiently in this direction. However, most commercially available sensors have a width/height aspect ratio of 4:3, 5:4 or 16:9, each of which ratios features a relatively large height dimension versus width. Thus, when such a sensor is used to cover the width of the conveyor belt (and maximum object width at all heights), the field of view in the transport direction is usually much larger than desired.
Thus prior art, conventional single camera vision system for logistics applications disadvantageously use a large number of pixels inefficiently. This inefficiency results from the fixed opening angle of the camera and the aspect ratio of the sensor. However, adjusting the camera assembly's viewing angle to suit a given height of object is challenging, both in terms of accuracy and speed of adjustment.